# Troubleshooting¶

## Library dependency issues¶

If you are experiencing problems with PyPSA or with the importing of the libraries on which PyPSA depends, please first check that you are working with the latest versions of all packages.

See Upgrade all packages to the latest versions and Upgrading PyPSA.

## Consistency check on network¶

Running `network.consistency_check()`

will examine the network
components to make sure that all components are connected to existing
buses and that no impedances are singular.

## Problems with power flow convergence¶

If your `network.pf()`

is not converging there are two possible reasons:

- The problem you have defined is not solvable (e.g. because in reality you would have a voltage collapse)
- The problem is solvable, but there are numerical instabilities in the solving algorithm (e.g. Newton-Raphson is known not to converge even for solvable problems; or the flat solution PyPSA uses as an initial guess is too far from the correction solution because of transformer phase-shifts)

There are some steps you can take to distinguish these two cases:

- Check the units you have used to define the problem are correct; see Conventions. If your units are out by a factor 1000 (e.g. using kW instead of MW) don’t be surprised if your problem is no longer solvable.
- Check with a linear power flow
`network.lpf()`

that all voltage angles differences across branches are less than 40 degrees. You can do this with the following code:

```
import pandas as pd, numpy as np
now = network.snapshots[0]
angle_diff = pd.Series(network.buses_t.v_ang.loc[now,network.lines.bus0].values - network.buses_t.v_ang.loc[now,network.lines.bus1].values,index=network.lines.index)
(angle_diff*180/np.pi).describe()
```

- You can seed the non-linear power flow initial guess with the
voltage angles from the linear power flow. This is advisable if you
have transformers with phase shifts in the network, which lead to
solutions far away from the flat initial guess of all voltage angles
being zero. To seed the problem activate the
`use_seed`

switch:

```
network.lpf()
network.pf(use_seed=True)
```

- Reduce all power values
`p_set`

and`q_set`

of generators and loads to a fraction, e.g. 10%, solve the load flow and use it as a seed for the power at 20%, iteratively up to 100%.

## Pitfalls/Gotchas¶

Some attributes are generated dynamically and are therefore only copies. If you change data in them, this will NOT update the original data. They are all defined as functions to make this clear.

For example:

`network.branches()`

returns a DataFrame which is a concatenation of`network.lines`

and`network.transformers`

`sub_network.generators()`

returns a DataFrame consisting of generators in`sub_network`

## Reporting bugs/issues¶

Please do not contact the developers directly.

Please report questions to the mailing list.

If you’re relatively certain you’ve found a bug, raise it as an issue on the PyPSA Github Issues page.